arXiv subjects

Raphina Liu

Publications and source records attributed to Raphina Liu.

4 recordsLinked to original sources

arXiv2026

EPISTEME: Trustworthy Coding Agents Through Tool Call Intent

Coding agents change software through tool calls. Tool calls mutate the local state. For good? For bad? Nobody knows. Agents never say why a tool call is made. We propose the epistemic agent design, in which the harness guarantees that the model declares why a tool call is made. The harness then records what the command actually did and checks the self-reported declaration against these effects. The result is a journal 1) that can guard calls before they run and 2) that supports audits and forensics. For evaluation, we use ImpossibleBench, whose tasks can be passed only by cheating, and demonstrate that forcing the agents to say why results in exposed cheating. Across 49 reward-hacking runs of two open-weight models, the journal exposed 90%.

cs.SE↗

arXiv2026

Software Supply Chain Smells: Lightweight Analysis for Secure Dependency Management

Modern software systems heavily rely on third-party dependencies, making software supply chain security a critical concern. We introduce the concept of software supply chain smells as structural indicators that signal potential security risks. We design and evaluate Dirty-Waters, a novel tool for detecting such smells in the supply chains of software packages. Through interviews with practitioners, we show that our proposed smells align with real-world concerns and capture signals considered valuable. A quantitative study of popular packages in the Maven and NPM ecosystems reveals that while smells are prevalent in both, they differ significantly across ecosystems, with traceability and signing issues dominating in Maven and most smells being rare in NPM, due to strong registry-level guarantees. Software supply chain smells support developers and organizations in making informed decisions and improving their software supply chain security posture.

cs.SE↗

arXiv2025

On-Chain Analysis of Smart Contract Dependency Risks on Ethereum

In this paper, we present the first large-scale empirical study of smart contract dependencies, analyzing over 41 million contracts and 11 billion interactions on Ethereum up to December 2024. Our results yield four key insights: (1) 59% of contract transactions involve multiple contracts (median of 4 per transaction in 2024) indicating potential smart contract dependency risks; (2) the ecosystem exhibits extreme centralization, with just 11 (0.001%) deployers controlling 20.5 million (50%) of alive contracts, with major risks related to factory contracts and deployer privileges; (3) three most depended-upon contracts are mutable, meaning large parts of the ecosystem rely on contracts that can be altered at any time, which is a significant risk, (4) actual smart contract protocol dependencies are significantly more complex than officially documented, undermining Ethereum's transparency ethos, and creating unnecessary attack surface. Our work provides the first large-scale empirical foundation for understanding smart contract dependency risks, offering crucial insights for developers, users, and security researchers in the blockchain space.

cs.SE↗

arXiv2024

Dirty-Waters: Detecting Software Supply Chain Smells

Using open-source dependencies is essential in modern software development. However, this practice implies significant trust in third-party code, while there is little support for developers to assess this trust. As a consequence, attacks have been increasingly occurring through third-party dependencies. These are called software supply chain attacks. In this paper, we target the problem of projects that use dependencies while unaware of the potential risks posed by their software supply chain. We define the novel concept of software supply chain smell and present Dirty-Waters, a novel tool for detecting software supply chain smells. We evaluate Dirty-Waters on three JavaScript projects across nine versions and demonstrate the prevalence of all proposed software supply chain smells. Not only are there smells in all projects, but there are many of them, which immediately reveal potential risks and provide clear indicators for developers to act on the security of their supply chain.

cs.SE↗